PDF(Pubmed)
Abstract：
Protein developability is requisite for use in therapeutic, diagnostic, or industrial applications. Many developability assays are low throughput, which limits their utility to the later stages of protein discovery and evolution. Recent approaches enable experimental or computational assessment of many more variants, yet the breadth of applicability across protein families and developability metrics is uncertain. Here, three library-scale assays-on-yeast protease, split green fluorescent protein (GFP), and non-specific binding-were evaluated for their ability to predict two key developability outcomes (thermal stability and recombinant expression) for the small protein scaffolds affibody and fibronectin. The assays\' predictive capabilities were assessed via both linear correlation and machine learning models trained on the library-scale assay data. The on-yeast protease assay is highly predictive of thermal stability for both scaffolds, and the split-GFP assay is informative of affibody thermal stability and expression. The library-scale data was used to map sequence-developability landscapes for affibody and fibronectin binding paratopes, which guides future design of variants and libraries.
摘要：
蛋白质显影性是用于治疗的必要条件，诊断,或工业应用。许多可显影性测定是低通量的，这将它们的效用限制在蛋白质发现和进化的后期阶段。最近的方法可以对更多的变体进行实验或计算评估，然而，跨蛋白质家族和可开发性指标的适用性的广度是不确定的。这里,三种文库规模的检测-酵母蛋白酶，分裂绿色荧光蛋白（GFP），和非特异性结合-评估了它们预测小蛋白支架亲和体和纤连蛋白的两个关键发育结果(热稳定性和重组表达)的能力。通过在文库规模的测定数据上训练的线性相关和机器学习模型来评估测定的预测能力。酵母上的蛋白酶测定高度预测两种支架的热稳定性，分裂GFP测定法提供了亲和体热稳定性和表达的信息。文库规模的数据用于绘制亲和体和纤连蛋白结合互补位的序列发育性景观，指导未来的变体和库的设计。
